Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands

• We developed a model to assess glyphosate resistance risk (GRR) in Johnsongrass (Sorghum halepense (L.) Pers.).
• The model assessed the influence of ecological and management drivers on GRR.
• The model shoed the highest sensitivity to changes in both seasonal average temperature and the dominance of glyphosate use.
• Model application to Argentina's cropping area showed a wide range of GRR values.
• The lowest GRR range values were represented in only 5.5% of the cropping area.

A fuzzy-logic based model was built in order to assess the relative influence of different ecological and management drivers on glyphosate resistance risk (GRR) in Sorghum halepense (L.) Pers. The model was hierarchically structured in a bottom-up manner by combining 16 input variables throughout a logical network. Input data were related to 1) herbicide usage, 2) crop rotation, 3) landscape characterization, 4) weed dispersal, and 5) mean maximum and minimum seasonal temperature. Mean maximum and minimum seasonal temperatures and the dominance of glyphosate use were the variables that showed the highest sensitivity to input changes. Application of the model at a regional scale resulted in a wide range of GRR values. The lowest range values (lower than 0 and between 0 and 0.25) were represented in 5.5% and 21.5% of the cropping area, respectively. Intermediate GRR range (between 0.25 and 0.5) were assessed in 57.3% of the cropping area whilst the highest GRR range values (0.5–0.7) were represented in only 15.6% of the studied area. The assessment of trade-offs between different ecosystem functions through expert opinion can complement traditional analyses for predicting herbicide resistance risk based on solely the genetic aspect of the evolutionary process.