Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative

Abstract

Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistant strains, reducing long-term disease control. When resistance is qualitative and complete - i.e. resistant strains are unaffected by the chemical and resistance requires only a single genetic change - using the lowest possible dose ensuring sufficient control is well-known as the optimal resistance management strategy. However, partial resistance (where resistant strains are still partially suppressed by the fungicide) and quantitative resistance (where a range of resistant strains are present) remain ill-understood. Here we use a model of quantitative fungicide resistance (parameterised for the economically-important fungal pathogen Zymoseptoria tritici) which handles qualitative partial resistance as a special case. We show that - for both qualitative partial resistance and quantitative resistance - although low doses are optimal for resistance management, for some model parameterisations the benefit does not outweigh the improvement in control from increasing doses. Via a machine learning approach (a gradient-boosted trees model combined with Shapley values to facilitate interpretability) we interpret the effect of parameters controlling pathogen mutation and characterising the fungicide, in addition to the timescale of interest.