North-Central Section - 50th Annual Meeting - 2016

Paper No. 14-5
Presentation Time: 3:10 PM

NUTRIENT LOAD REDUCTION THROUGH WATERSHED-SCALE COVER CROPPING: A HIGH RESOLUTION ANALYSIS


BRUENING, Ben G., Department of Geography and Geology, Illinois State University, Campus Box 4400, Normal, IL 61790-4400, bgbruen@ilstu.edu

Nutrient pollution originating from agricultural regions in the Midwest is a serious issue, leading to pollution of drinking water sources as well as large hypoxic zones in the Gulf of Mexico. One method that has been shown to reduce this pollution is the planting of winter cover crops. Winter cover crops such as rye and tillage radish have been shown to significantly reduce nitrate and phosphorous exported from agricultural fields, even in tile drained watersheds, which are resistant to nitrate management methods such as riparian zones. However, most studies take place in small agricultural study fields, sometimes with low resolution water sampling (weekly or bi-weekly). In this study, we are looking into the effectiveness of winter cover crops in reducing nutrient loading from tile drained agricultural watersheds in Central Illinois. We compare nitrate loading from two large agricultural watersheds (1000 acres and 700 acres), one of which is treated with cover crops, a combination of rye and tillage radish, while the other is not. To obtain high-resolution discharge and nitrate concentration, we use automated discharge measurements and automated water samplers at both of these watersheds. These samples are analyzed chemically using flow injection analysis (FIA). By comparing nutrient content and discharge between the treated and untreated watersheds, we will determine whether cover cropping reduces nutrient loading from tile-drained systems on a watershed basis, with the secondary goal of comparing measurements obtained with auto sampling equipment to those obtained via bi-weekly sampling. Preliminary results indicate that cover cropping is significantly reducing nutrient loading and that our automated sampling systems are collecting more detailed, accurate data than traditional bi-weekly sampling.