Background, aim and scope Agricultural landscapes comprise cultivated fields and semi-natural areas. Biological components of these compartments such as weeds, insect pests and pathogenic fungi can disperse sometimes over very large distances, colonise new habitats via insect flight, spores, pollen or seeds and are responsible for losses in crop yield (e.g. weeds, pathogens) and biodiversity (e.g. invasive weeds). The spatiotemporal dynamics of these biological components interact with crop locations, successions and management as well as the location and management of semi-natural areas such as roadverges. The objective of this investigation was to establish a modelling and simulation methodology for describing, analysing and predicting spatiotemporal dynamics and genetics of biological components of agricultural landscapes. The ultimate aim of the models was to evaluate and propose innovative cropping systems adapted to particular agricultural concerns. The method was applied to oilseed rape (OSR) volunteers playing a key role for the coexistence of genetically modified (GM) and non-GM oilseed rape crops, where the adventitious presence of GM seeds in non-GM harvests (AGMP) could result in financial losses for farmers and cooperatives. Material and methods A multi-year, spatially explicit model was built, using field patterns, climate, cropping systems and OSR varieties as input variables, focusing on processes and cultivation techniques crucial for plant densities and pollen flow. The sensitivity of the model to input variables was analysed to identify the major cropping factors. These should be modified first when searching for solutions limiting gene flow. The sensitivity to model processes and species life-traits were analysed to facilitate the future adaptation of the model to other species. The model was evaluated by comparing its simulations to independent field observations to determine its domain of validity and prediction error. Results The cropping system study determined contrasted farm types, simulated the current situation and tested a large range of modifications compatible with each farm to identify solutions for reducing the AGMP. The landscape study simulated gene flow in a large number of actual and virtual field patterns, four combinations of regional OSR and GM proportions and three contrasted cropping systems. The analysis of the AGMP rate at the landscape level determined a maximum acceptable GM OSR area for the different cropping systems, depending on the regional OSR volunteer infestation. The analysis at the field level determined minimum distances between GM and non-GM crops, again for different cropping systems and volunteer infestations. Discussion The main challenge in building spatially explicit models of the effects of cropping systems and landscape patterns on species dynamics and gene flow is to determine the spatial extent, the time scale, the major processes and the degree of mechanistic description to include in the model, depending on the species characteristics and the model objective. Conclusions These models can be used to study the effects of cropping systems and landscape patterns over a large range of situations. The interactions between the two aspects make it impossible to extrapolate conclusions from individual studies to other cases. The advantage of the present method was to produce conclusions for several contrasted farm types and to establish recommendations valid for a large range of situations by testing numerous landscapes with contrasted cropping systems. Depending on the level of investigation (region or field), these recommendations concern different decision-makers, either farmers and technical advisors or cooperatives and public decision-makers. Recommendations and perspectives The present simulation study showed that gene flow between coexisting GM and non-GM varieties is inevitable. The management of OSR volunteers is crucial for containing gene flow, and the cropping system study identified solutions for reducing these volunteers and ferals in and outside fields. Only if these are controlled can additional measures such as isolation distances between GM and non-GM crops or limiting the proportion of the region grown with GM OSR be efficient. In addition, particular OSR varieties contribute to limit gene flow. The technical, organisational and financial feasibility of the proposed measures remains to be evaluated by a multi-disciplinary team.