The spatial and temporal structure of neonicotinoid groundwater contamination in Wisconsin’s Central Sands vegetable growing region

Monday, June 1, 2015: 10:26 AM
Konza Prairie (Manhattan Conference Center)
Benjamin Bradford , Entomology, University of Wisconsin, Madison, WI
Russell L. Groves , Entomology, University of Wisconsin, Madison, WI
Neonicotinoids are a popular and widely-used class of insecticides whose water-soluble nature and 20-year usage history has led to questions about their accumulation in groundwater resources.  Wisconsin  vegetable growers continue to rely heavily on neonicotinoid insecticides for the control of damaging populations of key insect pests through a combination of in-ground and foliar applications.  Thiamethoxam, one of the three most widely-used neonicotinoids, has been detected in Wisconsin monitoring wells at concentrations nearing 9.0 µg/L and also in private potable wells where levels have not exceeded 1.61 µg/L.  The reported risks to human health at these levels is considered minor, however previous research has demonstrated quantifiable, sub-lethal effects on beneficial insect species when exposed to similar levels in pollen and nectar.  To date, no analysis had been conducted to quantify the spatial distribution or granularity of these detects in central Wisconsin’s groundwater, or to correlate crop use and pesticide inputs with specific well-site detections.  In cooperation with six commercial crop producers, 69 active irrigation wells clustered at two spatial scales (10 km; 50 km) in the Wisconsin River drainage basin were sampled, and thiamethoxam concentrations were determined using competitive ELISA kits.  72% of high-capacity irrigation wells returned positive detects (> 0.05 µg/L), with a maximum detect of 1.77 µg/L, and significant between and within cluster (well-to-well) variation was observed.  Sets of wells resampled over a one year period reveal seasonal changes at well sites.  Currently, relationships between these groundwater detections and several agricultural, hydrological, and temporal predictor variables are being explored.