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GeneTraMP, a spatio-temporal model of the dispersal and persistence of transgenes in feral, volunteer and crop plants of oilseed rape and related species

GeneTraMP, a spatio-temporal model of the dispersal and persistence of transgenes in feral, volunteer and crop plants of oilseed rape and related spec

GeneTraMP, a spatio-temporal model of the dispersal and persistence of transgenes in feral, volunteer and crop plants of oilseed rape and related species   (Citations: 7)
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We present a mechanistic simulation model (GeneTraMP), which allows analysing the persistence and dispersal of genetically modified plants (GMP) in heterogeneous landscapes. The modelling approach focuses on a spatial explicit representation of both, individual feral and volunteer plants as well as aggregated plant cohorts of crop plants and their reproduction units (pollen and seeds). The plant representation is set up to differentiate several crop species and related wild species. A variable number of genes can be considered, enabling to study a variety of questions related to gene flow, persistence and stacking of genetically modified traits in agricultural and natural landscapes. Different management schemes can be included to quantify and evaluate the outcome of small-scale interactions of natural processes and human activities in relation to various issues of concern in the current debate on GMP concerning environmental impact, biodiversity issues and the coexistence of GM and conventional crops.In this paper we present the general model setup, model structure and the implementation of basic processes concerning spatial configuration, plant development and dispersal of pollen and seeds. As far as possible, these were implemented by applying existing and previously validated model descriptions. The parameterisation was done for oilseed rape (OSR) and the region of northern Germany. Simulation results are presented to provide first insights into the potential of the model to represent and analyse many relevant aspects related to the introduction of genetically modified (GM) OSR crops in agriculture. The model also provides a basis for a spatial extrapolation when being run with appropriate data sets that represent the regional variability of relevant input parameter.
Journal: Ecological Indicators - ECOL INDIC , vol. 11, no. 4, pp. 974-988, 2011
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