Agriculture is currently the leading cause of stream impairment in the United States. As the population continues to grow as well as the demand for biofuels, more pressure is being exerted on land to produce greater quantities of food. To satisfy the need for increased production marginal forest and grasslands have been converted to agriculture, fertilizers and equipment have rapidly evolved, and land has been taken out of conservation programs. Unfortunately, water quality impairment often accompanies these efforts to increase crop production. To reduce the impacts of agriculture on water quality, best management practices (BMPs) have been developed and tested at the field scale, with fewer studies focusing on the effects of agricultural land cover and BMPs (e.g., riparian buffers) on water quality at the watershed scale. Thus, a study was designed to assess the effects of riparian buffers and agricultural land cover on water quality at the watershed scale. Within Richland and Silver Creek watersheds, tributaries of the Lower Kaskaskia River Watershed in Illinois, forty-three catchments ranging from 12 to 50 km2 were selected across an agricultural to urban land cover gradient. Between January 18, 2008 and August 3, 2009, grab samples were collected twice a month during the wet portion of the year and once a month during the dry portion of the year and analyzed for nutrients (ammonium, nitrate, and orthophosphate), bacteria (total coliform, fecal coliform, and E. coli), and total suspended solids (TSS). Correlation analyses were performed on the data to determine relationships between the water quality variables, whole-catchment land cover (agriculture, forest, and urban), and percent forest canopy cover within 50 m of the stream using two different stream layers (National Hydrologic Dataset (NHD), and Flow Accumulation Boundaries (FAB)). Also, riparian buffer characteristics were quantified in headwater streams to determine if they were more highly correlated with water quality variables than in larger order streams. The percent of agricultural land cover within a watershed was significantly correlated with TSS (r = 0.4556, p = 0.0021) and ammonium-N (r = 0.3043, p = 0.0473) during baseflow, and TSS (r = 0.2837, p = 0.0652), ammonium-N (r = 0.5306, p = 0.0003), nitrate-N (r = 0.2654, p = 0.0854), and orthophosphate (r = 0.3783, p = 0.0124) during stormflow. Total amount of enrolled Conservation Reserve Program (CRP) land within Richland Creek and Silver Creek watersheds were found not to be correlated with water quality. A possible reason for these results could be because only a very small percent of lands in Richland Creek and Silver Creek were enrolled in CRP. Whole-catchment land cover in most cases explained more variance than percent forest canopy cover within 50 m of streams for the water quality parameters analyzed. There were only slight differences between the two stream layers (NHD and FAB). However, the headwater streams of the FAB stream layer explained more variance in critical water quality parameters, ammonium-N (r = -0.5309, p = 0.002) during baseflow and ammonium-N (r = -0.6107 p <0.0001), and orthophosphate (r = -0.5273 p = 0.0003) during stormflow. Having an understanding of the impacts that riparian buffers and headwater streams have on water quality is key for watershed managers to focus restoration efforts in the most critical areas for maintaining stream quality.