Phosphorus loss in runoff from agricultural fields has been identified as an important contributor to eutrophication. The objective of this research was to determine the relationship between phosphorus (P) in runoff from a benchmark soil (Cecil sandy loam; fine, kaolinitic, thermic Typic Kanhapludult) and Mehlich III-, deionized water-, and Fe(2)O(3)-extractable soil P, and degree of phosphorus saturation (DPS). Additionally, the value of including other soil properties in P loss prediction equations was evaluated. Simulated rainfall was applied (75 mm h(-1)) to 54 1-m(2) plots installed on six fields with different soil test phosphorus (STP) levels. Runoff was collected in its entirety for 30 min and analyzed for total P and dissolved reactive phosphorus (DRP). Soil samples were collected from 0- to 2-, 0- to 5-, and 0- to 10-cm depths. The strongest correlation for total P and DRP occurred with DPS (r(2) = 0.72). Normalizing DRP by runoff depth resulted in improved correlation with deionized water-extractable P for the 0- to 10-cm sampling depth (r(2) = 0.81). The STP levels were not different among sampling depths and analysis of the regression equations revealed that soil sampling depth had no effect on the relationship between STP and P in runoff. For all forms of P in runoff and STP measures, the relationship between STP and runoff P was much stronger when the data were split into groups based on the ratio of oxalate-extractable Fe to Al. For all forms of P in runoff and all STP methods, R(2) increased with the inclusion of oxalate-extractable Al and Fe in the regression equation. The results of this study indicate that inclusion of site-specific information about soil Al and Fe content can improve the relationship between STP and runoff P.