Decision support systems (DSS) are used for weed control decisions worldwide. Several DSS for weed management have been published. However they mostly rely on full herbicide dosages and do not take weed population dynamics into account. We developed a modular DSS for long-term Avena fatua L. control in winter wheat. The DSS was parameterized with three year field experiment datasets covering yield loss data, densitydependent population dynamics data as well as data on dose dependent herbicide efficacy and dosedependent population dynamics. The DSS aims to control the A. fatua in the long run. Our hypothesis is that the optimized DSS reduces herbicide input while keeping the population density at low level, maintaining high grain yields and net return. The DSS comprises four sub-models calculating crop yield loss, A. fatua population dynamics as well as dose dependent herbicide efficacy and economics of the weed control decision. The economic sub-model calculates net return in dependency of the herbicide dosage and thus the resulting crop yield. First results of a 10-year simulation showed that herbicide input could be reduced by 40% compared to the economic threshold strategy, while the population density of A. fatua is controlled. Up to now the DSS has been parameterized for the herbicides Ralon Super, Axial 50 and Broadway. The results show the great potential of reducing herbicide input and point out the importance of including population dynamics models into DSS.