Browsing by Author "Lutman, P. J. W."

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    A model to simulate yield losses in winter wheat caused by weeds, for use in a weed management decision support system
    (Elsevier Science B.V., Amsterdam., 2010-11-01T00:00:00Z) Benjamin, L. R.; Milne, Alice E.; Parsons, David J.; Lutman, P. J. W.
    The 'within-season' module of the Weed Manager decision support system (DSS) predicts the effect of twelve UK arable weeds on winter wheat yields and profitability.  The model and decision algorithm that underpin the DSS are described and their performance discussed.  The model comprises: (i) seedling germination and emergence, (ii) early growth, (iii) phenological development, (iv) herbicide and cultivation effects and (v) crop yield loss.  Crop and weed emergence are predicted from the timing and method of cultivation, species biology, and the weather.  Wheat and weeds compete for resources, and yield losses are predicted from their relative leaf area at canopy closure.  Herbicides and cultural control methods reduce weed green area index, improving crop yield.  A decision algorithm identifies economically successful weed management strategies based on model output.  The output of the Weed Manager model and decision algorithm was extensively validated by experts, who confirmed the predicted responses to herbicide application were sufficiently accurate for practical use. Limited independent data were also used in the validation. The development of the module required integrating novel and existing approaches for simulating weed seedling establishment, plant development and decision algorithm design.  Combining these within Weed Manager created a framework suitable for commercial use.
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    Using stochastic dynamic programming to support weed management decisions over a rotation
    (Blackwell Publishing Ltd, 2009-04-01T00:00:00Z) Benjamin, L. R.; Milne, Alice E.; Lutman, P. J. W.; Parsons, David J.; Cussans, J.; Storkey, J.
    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.