A model to simulate yield losses in winter wheat caused by weeds, for use in a weed management decision support system

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. ► A Decision Support System has been devised to support weed control in winter wheat. ► The biological model comprises: (i) seedling germination and emergence, (ii) early growth, (iii) phenological development, (iv) herbicide and cultivation effects and (v) crop yield loss. ► A decision algorithm identifies economically successful weed management strategies based on the output from the biological model, using efficacy data from a range of herbicides and cultivation practices. ► The decision outputs were validated by experts and by field data. ► The development of the module required integrating novel and existing approaches for simulating weed seedling establishment, plant development and decision algorithm design into a single framework suitable for commercial use.