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

Por: Ferraro, D. O.
Colaborador(es): Ghersa, C. M.
ISSN: 0261-2194.Tipo de material: Artículos y capítulos. Recurso electrónico.Tema(s): FUZZY LOGIC | HERBICIDE RESISTANCE | RISK MODELING | AGRICULTURAL LAND | ASSESSMENT METHOD | ECOSYSTEM FUNCTION | FUZZY MATHEMATICS | GLYPHOSATE | HERBICIDE | LANDSCAPE | NUMERICAL MODEL | PESTICIDE RESISTANCE | SORGHUM | TRADE-OFF | ARGENTINA | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR. En: Crop Protection Vol. 51 (2013) 32-39Resumen: 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 percent and 21.5 percent of the cropping area, respectively. Intermediate GRR range [between 0.25 and 0.5] were assessed in 57.3 percent of the cropping area whilst the highest GRR range values [0.5-0.7] were represented in only 15.6 percent 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.
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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 percent and 21.5 percent of the cropping area, respectively. Intermediate GRR range [between 0.25 and 0.5] were assessed in 57.3 percent of the cropping area whilst the highest GRR range values [0.5-0.7] were represented in only 15.6 percent 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.

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