Development of a pest threshold decision support system for minimising damage to winter wheat from wheat bulb fly, Delia coarctata

Abstract

AbstractWheat bulb fly, Delia coarctata, is an important pest of winter wheat in the UK, causing significant damage of up to 4 t/ha. Accepted population thresholds for D. coarctata are 250 eggs/m2 for crops sown up to the end of October and 100 eggs/m2 for crops sown from November. Fields with populations of D. coarctata that exceed the thresholds are at higher risk of experiencing economically damaging infestations. In the UK, recent withdrawal of insecticides means that only a seed treatment (Signal 300 ES) is available for chemical control of D. coarctata; however, this is only effective for late‐sown crops and accurate estimations of annual population levels are required to ensure a seed treatment is applied if needed. As a result of the lack of postdrilling control strategies, the management of D. coarctata is becoming reliant on nonchemical methods of control. Control strategies that are effective in managing similar stem‐boring pests of wheat include sowing earlier and using higher seed rates to produce crops with greater pest tolerance. In this study, we develop two predictive models that can be used for integrated D. coarctata management. The first is an updated pest level prediction model that predicts D. coarctata populations from meteorological parameters with a predictive accuracy of 70%, a significant improvement on previous prediction models. Our second model predicts the maximum number of shoots for a winter wheat crop that would be expected at the terminal spikelet development stage. This shoot number model uses information about the thermal time from plant emergence to terminal spikelet, leaf phyllochron length, plant population and sowing date to predict the degree of tolerance a crop will have against D. coarctata. The shoot number model was calibrated against data collected from five field experiments and tested against data from four experiments. Model testing demonstrated that the shoot number model has a predictive accuracy of 65.7%. The foundation for a future decision support system using these models for the sustainable management of D. coarcata risk is described. It should be noted that these models represent a stepping‐stone towards a decision support system and that further model validation over a wider geographic range is required.