Modelling quantitative fungicide resistance and breakdown of resistant cultivars: designing integrated disease management strategies for Septoria of winter wheat

2022

Preprint

Self Cite

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.

Section: Model Explanationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Taylor

2022

Preprint

Self Cite

“…Disease-resistant plants can limit pathogen replication, whilst tolerant hosts are not damaged as severely by the pathogen (Pagán & García-Arenal (2018)). Both traits lie on a spectrum, and in practice plants are more likely to have only partial (“quantitative”) disease resistance or tolerance (Marchant et al (2020), French et al (2016), Corwin & Kliebenstein (2017), Taylor & Cunniffe (2022)). Previous work has shown that tolerant and resistant crops caused contrasting outcomes for non-controllers (Murray-Watson & Cunniffe (2022)).…”

Section: Introductionmentioning

confidence: 99%

Expanding growers’ choice of disease management options can promote suboptimal social outcomes

Murray-Watson

2022

Preprint

Previous models of growers' decision-making during epidemics have unrealistically limited disease management choices to just two options. Here, we expand previous game-theoretic models of grower decision-making to include three control options: crop that is either tolerant, resistant, or susceptible to disease. Using Tomato Yellow Leaf Curl Virus (TYLCV) as a case study, we investigate how growers can be incentivised to use different control options to achieve socially-optimal outcomes. To do this, we consider the efforts of a "social planner" who moderates the price of crops. We find that subsidising tolerant crop costs the social planner more in subsidies, as its use encourages selfishness and widespread adoption. Subsidising resistant crop, however, provides widespread benefits by reducing the prevalence of disease across the community of growers, including those that do not control, reducing the number of subsidies required from the social planner. We then use Gini coefficients to measure equitability of each subsidisation scheme. This study highlights how grower behaviour can be altered using crop subsidies to promote socially-optimal outcomes during epidemics.

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

Taylor

2023

J. R. Soc. Interface.

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.