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Derivation and testing of a model to predict selection for fungicide resistance
Author(s) -
Hobbelen P. H. F.,
Paveley N. D.,
Fraaije B. A.,
Lucas J. A.,
van den Bosch F.
Publication year - 2011
Publication title -
plant pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.928
H-Index - 85
eISSN - 1365-3059
pISSN - 0032-0862
DOI - 10.1111/j.1365-3059.2010.02380.x
Subject(s) - fungicide , powdery mildew , biology , hordeum vulgare , selection (genetic algorithm) , blumeria graminis , azoxystrobin , mildew , statistics , toxicology , horticulture , agronomy , poaceae , mathematics , plant disease resistance , genetics , artificial intelligence , computer science , gene
A mathematical model was derived to predict selection for fungicide resistance in foliar pathogens of cereal crops. The model was tested against independent data from four field experiments quantifying selection for the G143A mutation conferring resistance to a quinone outside inhibitor (QoI) fungicide in powdery mildew ( Blumeria graminis f.sp. hordei ) on spring barley ( Hordeum vulgare ). Fungicide treatments with azoxystrobin differed in the total applied dose and spray number. For each treatment, we calculated the observed selection ratio as the ratio of the frequency of the resistant strain after the last and before the first spray. The model accurately predicted the variation in observed selection ratios with total applied fungicide dose and number of sprays for three of the four experiments. Underprediction of selection ratios in one experiment was attributed to the particularly late epidemic onset in that experiment. When the equation representing epidemic development was modified to account for the late epidemic, predicted and observed selection ratios at that site were in close agreement. On a scatter plot of observed selection ratios on predicted selection ratios, for all four experiments, the 1:1 line explained 89–92% of the variance in the mean of observed selection ratios. To our knowledge, this is the first fungicide resistance model for plant pathogens to be rigorously tested against field data. The model can be used with some degree of confidence, to identify anti‐resistance treatment strategies which are likely to be effective and would justify the resources required for experimental testing.