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Quantitative macropore characterizations were performed in large zero-tension soil lysimeters of a Maury silt loam (fine, mixed, mesic Typic Paleudalf) and a Loradale silt loam (fine, silty, mixed, mesic Typic Axgiudoll) soil in an effort to assess potential colloid transport. Steel pipe sections (50 cm diameter X 100 cm length) were hydraulically driven into the soil for lysimeter establishment. A dye tracer was applied into each lysimeter under saturated conditions at eight-hour intervals using 500 mL pulse applications. After one pore volume elution, the center section of the stained lysimeters was removed, dissected, and photographed. Kubiena tins were inserted into the lysimeter's remaining soil at 15 cm intervals. Vertically oriented thin sections were prepared from each tin. Pore area, diameter, and orientation were evaluated from thin sections and photographs using commercial grade

Terrain analysis can be used to locate concentrated flow erosion e.g., ephemeral gully erosion across landscapes. For example, studies have found that ephemeral gullies were likely to occur when field specific thresholds were exceeded for the following terrain attributes the product of upslope area, slope, and plan curvatures [ ] topographic wetness index, upslope area, and slope [ ] and the topographic wetness index and the product of the upslope area and slope [ ]. "nother study used a cartographic classification and threshold procedure for erosion channel identification [ ]."n alternative approach utilized logistic regression and artificial neural network procedures to predict where erosion channels would appear in agricultural fields based on digital terrain attributes [ ]. With leave-one-field-out validation, it was determined that the more simple logistic regression was more appropriate because it performed as well as the non-linear neural network procedure. In a follow up study, erosion channels predicted from terrain attributes derived from -m US Geological Survey USGS digital elevation models DEMs were compared to those derived from DEMs created with survey-grade real-time kinematic RTK Global Positioning System GPS data [ ]. The USGS models identified most eroded features but the RTK analyses delineated them more clearly. The authors concluded that the USGS predictions were adequate for many agricultural applications because creating DEMs with RTK was relatively costly while USGS data was freely available on the Internet for most of the United States. " graphical representation illustrates how the logistic regression analysis [ , ] can be fit step and then applied step in Figure .