54
Breckling, B., Reuter, H. & Verhoeven, R. (2008) Implications of GM-Crop Cultivation at Large Spatial Scales.
Theorie in der Ökologie 14. Frankfurt, Peter Lang.
Modelling maize cross-pollination probabilities on the regional level –
exemplary simulations for the county Elbe Elster in Brandenburg,
Germany
Hauke Reuter1,2, Broder Breckling1, Angelika Wurbs3 & Karen Höltl3
(1Dept. General and Theoretical Ecology, Centre for Environmental Research and
Sustainable Technology, University of Bremen, Germany, 2Centre for Marine Tropical
Ecology, Bremen, 3Leibniz-Centre for Agricultural Landscape Research (ZALF),
Müncheberg, Germany. – [email protected])
Keywords: Maize, Co-existence, GM-crops, cross-pollination, simulation model,
landscape
Introduction
Co-existence between agriculture with and without genetically modified (GM) varieties
is an important issue in European agriculture. EU-legislative requires that both should
be possible (EC 2001). To analyse co-existence implications explicitly requires the consideration of the landscape or the regional level in order to be able to calculate probabilities of admixture in the harvest for different cultivation situations. Bio-geographic
variability (e.g. landscape structure, management and cultivation regimes, varieties,
climate) plays an important role to assess potentially problematic combinations of
factors. However, despite this necessity the landscape level does not constitute an
integral part of the currently practised risk assessment for GMO.
Maize (Zea mays) is an important crop which reaches high cultivation densities in many
parts of Europe and elsewhere in the world. Fields are often in direct neighbourhood to
each other and cross-pollination between fields is an important concern as maize is
mainly wind-pollinated. Pollen are produced in high abundance. It is known that pollen
concentration decreases quickly beyond the field edge, however it is equally well
known that maize pollen is transported for several kilometres (Hofmann 2007; Kuparinen 2007; Brunet et al. 2008). Available approaches do not yet provide a straight
forward way as how to estimate cross-pollination probabilities on the regional scale.
Present models mainly focus on smaller areas (< 10 km2 e.g. Lipsius et al. 2006;
Angevin et al. 2008).
Expanding available options in this regard, we present a simulation programme which
applies a previously developed dispersal kernel (see Reuter et al. 2008) to simulate
cross-pollination probabilities for whole regions, requiring only a relative minimum of
input data and providing overall estimates for the region. It bases on trends which are
Modelling maize cross-pollination probabilities on the regional level
55
calculated in scenarios on a field-by-field level. Exemplary simulation results for the
county of Elbe-Elster in the federal state of Brandenburg, Germany are presented.
Modelling regional cross-pollination probabilities
The model we present here simulates cross-pollination between fields to give an
estimate of average cross-pollination rates and the according variability under regionspecific conditions. It relates e.g. the rate of GM-cultivation, maize field density, field
size, and the window of flowering dates. It can handle maps containing several ten
thousands of fields which is sufficient to represent entire regions.
The model uses a simplified field geography. Each field is represented as a circular
area. Field centroid distances are used to calculate the potential for mutual pollination
impact. For each field a simplified phenological development is simulated which includes the time needed until flowering and the duration of the flowering phase. Respective
average values are given as parameters together with their standard variation which
quantifies the stochasticity for both processes.
Cross-pollination levels are determined with the dispersal kernel which is directly
derived from the analysis of empirical studies (see Reuter et al. 2008). The model uses a
table function as input, which can be modified by the user. This allows to study the
effect of modifications in the underlying functional relationship of cross-pollination
levels and field distances. In the current simulations we applied the results obtained
from the data analysis presented by Reuter et al. 2008. Distant dependent cross-pollination rates are subject to a stochastic influence which corresponds to the standard
variation derived from the data analysis.
For each field, the impact of all neighbours within a defined maximum distance is calculated. Effective pollen input rates are obtained by multiplying the distance dependent
unit rates with size factors of the involved donor and receptor field. The thus obtained
input rates are summed and then normalised to get admixture rates for the harvest.
Results obtained for an application example in Eastern Germany
Exemplary model simulations were performed for several counties in the federal state of
Brandenburg in the East of Germany. The scenarios developed by Höltl & Wurbs
(2008) were applied for three levels of GM-maize cultivation (10, 40 and 70 %). These
scenarios present typical situations in each of the counties with respect to the field sizes
and neighbourhood relations and the percentage of maize cultivation. Field map generation and allocation of GM-fields kept a 150 metre zone free of GM fields around each
field with conventional maize and 300 metre around fields with organic production.
Simulations were repeated 50 times and averages were calculated. Figure 1 illustrates an
exemplary result for a single simulation run for the county of Elbe Elster with a GM
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Hauke Reuter et al.
cultivation share of 10 %. The scenario comprised more than 700 field maize fields
ranging from 2 to 102 hectare with 58 % of the fields less than 10 hectare in size.
The simulation results indicated a strong dependence of cross-pollination probabilities
in conventional fields on the overall fraction (%) of GM fields (Fig. 2). For the county
of Elbe Elster the rate of fields containing a contamination above the current labelling
threshold of 0.9 % increases from 0.05 % to 4.77 % and 16.85 % for 10 %, 40 % and
70 % GM maize cultivation respectively.
Discussion and conclusions
The paper presents a modelling approach which allows to estimate cross-pollination
probabilities between maize fields on a regional level. This will become important in
order to estimate the potential for co-existence under different cultivation scenarios. The
exemplary simulations illustrate the extent and the variability of contamination problems on the regional level.
Fig. 1. Exemplary results for one simulation run for the county of Elbe Elster, 10 % GM-maize. The overall extent of
the county is 1 889 km² with an agricultural area of 972 km² and 673 km² forested area.
Modelling maize cross-pollination probabilities on the regional level
57
Fig. 2. Comparision of scenarios with different intensities of GM-maize cultivation for the county Elbe Elster
(Brandenburg, East Germany)
The presented results show, that cross-pollination rates on a regional scale depended on
the percentage of maize cultivation and landscape properties (e.g. field sizes and shape
of fields). However it has to be noted, that the simulations did not account for any seed
impurities or any other processes besides cross-pollination, which may lead to additional increase of GM harvest in conventional maize cultivation.
Together with the analysis programme (Reuter et al. 2008) the demonstrated approach
has a considerable flexibility. It is easy to incorporate new cross-pollination studies into
the analyses programme as soon as they are available. This will result in according
dispersal function modifications. Equally, scenario conditions can be specified in different ways without changing the programme, as well as different scenario maps can be
processed to represent other regions.
The combination of both software programmes (1 to calculate the dispersal kernel based
on an input data table and 2 to calculate mutual impact on a regional scale) provide a
novel approach which allows to investigate regional implications by defining scenario
data for specific conditions and study the influences of different assumptions on the
occurring gene flow.
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Hauke Reuter et al.
Acknowledgements: The authors gratefully acknowledge funding by the German
Federal Ministry for Research and Technology (BMBF) under grant FKZ: 0312637A.
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