This study describes a model that predicts the impact of weed management on the
population dynamics of arable weeds over a rotation and presents the economic
consequences. A stochastic dynamic programming optimisation is applied to the
model to identify the management strategy that maximises gross margin over the
rotation. The model and dynamic programme were developed for the weed management
decision support system -'Weed Manager'. Users can investigate the effect of
management practices (crop, sowing time, weed control and cultivation practices)
on their most important weeds over the rotation or use the dynamic programme to
evaluate the best theoretical weed management strategy. Examples of the output
are given in this paper, along with discussion on their validation. Through this
study, we demonstrate how biological models can (i) be integrated into a
decision framework and (ii) deliver valuable weed management guidance to users
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