Design of a decision support tool for managing coexistence between genetically modified and conventional maize at farm and regional levels

Abstract

Over the last 15years, several studies on coexistence have used simulation results of spatially explicit gene flow models. These models predict the adventitious presence (AP) of GM grains in non-GM fields at the landscape scale. However, result uncertainty is not quantified. Moreover, most of the models require an important amount of input data on climate, land use, and crop management practices which might not always be available. A comprehensive Bayesian statistical approach has been implemented in the case of gene flow. This approach makes it possible to inform the decision-maker on AP, whatever the amount of information available in a given situation, to provide information on the uncertainty of the predictions and to model the variability of AP within a field, which helps set up sampling strategies.The resulting decision-support tool (DST) can compute the expected AP and its probability distribution in non-GM maize fields at different times of the growing season and under different management scenarios. Integrated through a web interface, the DST is designed to be operationally helpful for managing coexistence between GM and non-GM maize crops for a wide range of stakeholders from farmers to policy makers.