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 (parametrized for the economically important fungal pathogen Zymoseptoria tritici ) which handles qualitative partial resistance as a special case. Although low doses are optimal for resistance management, we show that for some model parametrizations the resistance management benefit does not outweigh the improvement in control from increasing doses. This holds for both qualitative partial resistance and quantitative resistance. 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 time scale of interest.

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“…These markers were then utilized to identify additional bacteria exhibiting the desired activity, thereby enhancing the efficacy of discovering antagonistic strains (Biggs et al, 2021 ). Taylor utilized a blend of machine learning techniques and epidemiological methods to identify the most effective fungicide that elicits bacterial resistance at minimal levels, with the fungal pathogen Zymoseptoria tritici serving as a case study (Taylor and Cunniffe, 2023 ). Likewise, Zhang et al integrated metadata analysis with machine learning techniques to detect both diseased and disease-suppressive soils by utilizing 54 biomarker genera.…”