Abstract:
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.
