Combating insecticide resistance in major UK pests


Cereals & Oilseeds
Project code:
01 January 2013 - 31 July 2016
AHDB Cereals & Oilseeds.
AHDB sector cost:
Total project value:
Project leader:
Dr Sacha White, Dr Joe Helps, Dr Ian Denholm, Catriona Walker, Dr Frank van den Bosch and Dr Neil Paveley ADAS Boxworth, Cambridgeshire CB23 4NN Rothamsted Research, West Common, Harpenden, Hertfordshire AL5 2JQ University of Hertfordshire, College Lane, Hatfield, Hertfordshire AL10 9AB ADAS Rosemaund, Preston Wynne, Hereford, HR1 3PG ADAS High Mowthorpe, Duggleby, Malton, North Yorkshire YO17 8BP


21120005-summary-report 21120005-final-report

About this project


Despite substantial progress with developing non-chemical methods of crop protection, pesticides remain essential for effective suppression of pests, pathogens and weeds in many cropping systems.

Reliance on pesticides introduces a number of risks, including the appearance of resistance in target organisms. The overall aim of this project was to maintain chemical control of economically important invertebrate pests of agriculture and horticulture, by identifying effective insecticide resistance management strategies for target-site resistance and developing an objective method for resistance risk assessment.

Work package 1 utilised a novel mathematical model to simulate the evolution of target-site resistance in crop pests with contrasting life-histories. Optimal management tactics to delay the development of target-site resistance were explored for groups of pest species with contrasting life histories. This produced two key findings:

Firstly, simulations demonstrated that in most scenarios tested, a higher dose of insecticide leads to faster selection for resistance resulting from a single target-site mutation.

Secondly, simulations were performed to identify the optimal combination of two insecticides with different modes of action (MoA), to which resistance from two target-site mutations (one for each MoA) was developing. These demonstrated that when two insecticides were applied together at their label dose in a mixture, resistance developed considerably faster than when the two insecticides were alternated. However, if the dose of each insecticide was reduced so that the mixture provided the same control of the insect population as a single label dose of either product alone, then mixtures were often the most effective resistance management tactic. Only when the resistance resulted in substantial fitness costs in the insect species did alternating two insecticides at their label dose lead to slower resistance development than reduced-dose mixtures.

These results are in agreement with findings from modelling and experimental studies on fungicide resistance, but need experimental validation. As neither metabolic nor multi-site resistance were considered it is not known whether their inclusion would affect the results. The conclusions on resistance management need to be interpreted also to take account of the practical requirement for robust control.

Work package 2 investigated the influence of biological, agronomic and insecticide-related traits on resistance risk. A data set of over 100 historical cases of resistance (comprised of target site and metabolic resistance cases) was used to test which traits were associated with faster or slower development of resistance.

Multivariate statistical methods were used to develop a resistance risk assessment model, which consisted of five traits (crop area, crop type, number of crop hosts, mode of reproduction and taxonomic order) and accounted for 45.9% of the variation in the speed at which resistance occurred.

The model can be used to guide resistance risk assessment for novel pest/crop combinations, since all the key traits are relatively easy to quantify without knowledge of prior resistance history.

Although, considerable uncertainty remains, the model provides an objective means of ranking pest-crop combinations from high to low risk, allowing proportionate resistance management strategies to be put in place. 


Determinants of optimal insecticide resistance management strategies (Journal of Theoretical Biology paper, 2020)