2013 Annual Report
1a.Objectives (from AD-416):
Quantify environmental and agricultural management practices that drive the spread of glyphosate-resistant common waterhemp in central Illinois.
1b.Approach (from AD-416):
We will focus on a 16 x 16 km area (100 sections) in south-central Illinois where the presence of glyphosate-resistant waterhemp is confirmed. We will work with a local agrichemical retail dealer/applicator to obtain current and historical data on agronomic management (including planting information, tillage practices, and chemical inputs) as well as current estimates of levels of waterhemp infestation and glyphosate-resistance status in fields comprising approximately 15,000 ha. A subset of these fields will be selected that comprise a range of management practices, landscape positions, and waterhemp infestation levels (including presence/absence of glyphosate resistance). Within this subset of fields, data will be collected on: a) waterhemp plant and seed population density within the field and in field margins, b) prevalence of glyphosate-resistant waterhemp plants and seeds in field and field margins, and c) geospatial coordinates. Greenhouse bioassays will be used to determine whether sampled plants are glyphosate resistant. Soil textural data for the fields will be extracted from a NRCS geographic information system (GIS) layer. Field landscape position, relative to features of interest (e.g. barriers and dispersal corridors within the landscape matrix such as forests, riparian areas, railroads and roads), will also be extracted from an NRCS GIS layer. Using fields as the experimental unit, presence/absence of mature, glyphosate-resistant waterhemp as the response variable, and the environmental and management variables described above as predictor variables, multiple logistic regression models will be fit using restricted maximum likelihood methods. Candidate models will be considered that include management variables, environmental variables, and a combination thereof. The most parsimonious models (i.e., those models that offer the best fit with fewest parameters) will be used to identify factors associated with low risk of glyphosate-resistance waterhemp establishment in production fields.
From farmer surveys on over 150 fields in central Illinois, coupled with greenhouse dose-response studies, we have assembled a dataframe on waterhemp herbicide resistance, agronomic management history, soil characteristics, and landscape features. We have begun to analyze this data set to quantify risk factors associated with the evolution of herbicide resistance on common waterhemp.