Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative.
J R Soc Interface
; 20(201): 20220685, 2023 04.
Article
en En
| MEDLINE
| ID: mdl-37073520
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Farmacorresistencia Fúngica
/
Aprendizaje Automático
Tipo de estudio:
Qualitative_research
Límite:
Humans
Idioma:
En
Revista:
J R Soc Interface
Año:
2023
Tipo del documento:
Article
Pais de publicación:
Reino Unido