Sediment removal by prairie filter strips in row-cropped ephemeral watersheds.

Twelve small watersheds in central Iowa were used to evaluate the effectiveness of prairie filter strips (PFS) in trapping sediment from agricultural runoff. Four treatments with PFS of different size and location (100% rowcrop, 10% PFS of total watershed area at footslope, 10% PFS at footslope and in contour strips, 20% PFS at footslope and in contour strips) arranged in a balanced incomplete block design were seeded in July 2007. All watersheds were in bromegrass ( L.) for at least 10 yr before treatment establishment. Cropped areas were managed under a no-till, 2-yr corn ( L.)-soybean [ (L.) Merr.] rotation beginning in 2007. About 38 to 85% of the total sediment export from cropland occurred during the early growth stage of rowcrop due to wet field conditions and poor ground cover. The greatest sediment load was observed in 2008 due to the initial soil disturbance and gradually decreased thereafter. The mean annual sediment yield through 2010 was 0.36 and 8.30 Mg ha for the watersheds with and without PFS, respectively, a 96% sediment trapping efficiency for the 4-yr study period. The amount and distribution of PFS had no significant impact on runoff and sediment yield, probably due to the relatively large width (37-78 m) of footslope PFS. The findings suggest that incorporation of PFS at the footslope position of annual rowcrop systems provides an effective approach to reducing sediment loss in runoff from agricultural watersheds under a no-till system.

Effects of watershed configuration and composition on downstream lake water quality.

We evaluated the relationships between landscape characteristics and lake water quality in receiving waters by regressing four water quality responses on landscape variables that were measured for whole watersheds and three different buffer distances (30, 60, and 120 m). Classical percolation theory was used to conceptualize nutrient pathways and to explain nonlinear responses. The response variables were total nitrogen (TN), total phosphorus (TP), chlorophyll-a (Chl-a), and Secchi transparency (SD). Landscape data were obtained from satellite image-derived maps of 130 watersheds in Iowa using geographic information systems software. We developed regression models with a stepwise protocol selecting the optimal number of significant explanatory variables. Configuration variables such as contagion, the cohesion of cropland and urban land, and the aggregation index of forest were very important and more important than variables assessing landscape composition (e.g., percentage farmland). Whole watershed models predicted between 15 and 67% of the variability in TN, TP, Chl-a, and SD. Proximity-explicit data offered only slightly improved statistical power over land cover data derived from the entire watershed for variables TN, Chl-a. and SD, but not for TP.