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Title:   Author  item
                                            RADCLIFFE, DAVID - University Of Georgiaitem
                                            FORSBERG, ADAM - University Of Georgiaitem
                                            Bolster, Carlitem
                                            MITTELSTET, AARON - Oklahoma State Universityitem
                                            STORM, DAN - Oklahoma State Universityitem
                                            RAMIREZ-AVILA, JOHN - Mississippi State Universityitem
                                            OSMOND, DEANNA - North Carolina State University
  Submitted to: Soil and Water Conservation Society
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 2/9/2016
                                    Publication  Date: 8/17/2016
                                    Citation: Radcliffe, D., Forsberg, A., Bolster, C.H., Mittelstet, A., Storm, D., Ramirez-Avila, J., Osmond, D. 2016. Evaluation of the TBET model for improving P-indices in southern states. Soil and Water Conservation Society. 48.Interpretive Summary:
                                Technical Abstract: Management of agricultural nonpoint source phosphorus (P) requires identification of fields susceptible to P loss. P-Indices are the most common tools used to identify critical source areas of P loss. However, the success of the P-index approach is impeded by insufficient measured P loss data. Simulated data from a quantitative P transport model may be used to modify a P-index for scenarios where P loss data is not available. The objective of this study was to compare predictions from the Texas Best Management Evaluation Tool (TBET) against measured P loss data to determine whether the model can be used to improve P-Indices in the South. Field-scale measured P loss data from study sites in AR, GA, and NC were used to assess the accuracy of TBET for predicting field-scale loss of P. Goodness of fit was measured using the Nash-Sutcliffe Efficiency (NSE) and Mean Absolute Error (MAE) statistics. We found that TBET can generate satisfactory event-based predictions (NSE = 0.3) of runoff, sediment and P loss with site-specific calibration. We also compared TBET predictions of average annual P loss against the three state P-indices for the sites in this study. Goodness-of-fit between measured average annual total P loss and the calibrated TBET model (MAE = 4.4 kg/ha/yr) was similar to the P-Indices (MAE = 6.9 kg/ha/yr). We conclude that a well-calibrated TBET model can be used to improve P-Indices for certain management scenarios. However, developing a version of TBET that can simulate all of the management, soil, and weather combinations that are likely to be encountered in a state will require extensive work.


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