Phosphorus (P) Indices in the southern United States frequently produce different recommendations for similar conditions. We compared risk ratings from 12 southern states (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Texas) using data collected from benchmark sites in the South (Arkansas, Georgia, Mississippi, North Carolina, Oklahoma, and Texas). Phosphorus Index ratings were developed using both measured erosion losses from each benchmark site and Revised Universal Soil Loss Equation 2 predictions; mostly, there was no difference in P Index outcome. The derived loss ratings were then compared with measured P loads at the benchmark sites by using equivalent USDA-NRCS P Index ratings and three water quality models (Annual P Loss Estimator [APLE], Agricultural Policy Environmental eXtender [APEX], and Texas Best Management Practice Evaluation Tool [TBET]). Phosphorus indices were finally compared against each other using USDA-NRCS loss ratings model estimate correspondence with USDA-NRCS loss ratings. Correspondence was 61% for APEX, 48% for APLE, and 52% for TBET, with overall P index correspondence at 55%. Additive P Indices (Alabama and Texas) had the lowest USDA-NRCS loss rating correspondence (31%), while the multiplicative (Arkansas, Florida, Louisiana, Mississippi, South Carolina, and Tennessee) and component (Georgia, Kentucky, and North Carolina) indices had similar USDA-NRCS loss rating correspondence-60 and 64%, respectively. Analysis using Kendall's modified Tau suggested that correlations between measured and calculated P-loss ratings were similar or better for most P Indices than the models. (Dubrovsky and Hamilton, 2010). Recent harmful algal blooms in Lake Erie caused Toledo to shut down its drinking water supply for several days, refocusing the link between nutrient enrichment (particularly phosphorus [P]) and water quality impairment (Stow et al., 2015), with many of these nutrients being agriculturally derived. To control agricultural nutrient loading to surface waters, multiple control strategies are necessary at the source and during transport into the receiving water resources. The USDA-NRCS refers to this as "avoid, control, and trap."Since the late 1990s, the USDA and USEPA jointly required all states to adopt a unified nutrient management policy through the NRCS Code 590 Standard (USDA and USEPA, 1999). States were required to establish a soil-test P threshold based on crop requirements (above which P applications were restricted), to establish an alternative soil test P threshold using water quality criteria, or to develop a P Index to identify fields at risk for P losses. Forty-eight states and some territories, including Puerto Rico, chose to use P Indices (Sharpley et al., 2003), a concept originally developed by USDA-NRCS for assigning relative risk of P loss to agricultural fields (Lemunyon and Gilbert, 1993). California and Connecticut use soil-test P crop response (Sharpley et al., 2003).To...

A wide range of mathematical models are available for predicting phosphorus (P) losses from agricultural fields, ranging from simple, empirically based annual time-step models to more complex, process-based daily time-step models. In this study, we compare field-scale P-loss predictions between the Annual P Loss Estimator (APLE), an empirically based annual time-step model, and the Texas Best Management Practice Evaluation Tool (TBET), a process-based daily time-step model based on the Soil and Water Assessment Tool. We first compared predictions of fieldscale P loss from both models using field and land management data collected from 11 research sites throughout the southern United States. We then compared predictions of P loss from both models with measured P-loss data from these sites. We observed a strong and statistically significant (p < 0.001) correlation in both dissolved (r = 0.92) and particulate (r = 0.87) P loss between the two models; however, APLE predicted, on average, 44% greater dissolved P loss, whereas TBET predicted, on average, 105% greater particulate P loss for the conditions simulated in our study. When we compared model predictions with measured P-loss data, neither model consistently outperformed the other, indicating that more complex models do not necessarily produce better predictions of field-scale P loss. Our results also highlight limitations with both models and the need for continued efforts to improve their accuracy.Comparing an Annual and a Daily Time-Step Model for Predicting Field-Scale Phosphorus Loss Carl H. Bolster,* Adam Forsberg, Aaron Mittelstet, David E. Radcliffe, Daniel Storm, John Ramirez-Avila, Andrew N. Sharpley, and Deanna Osmond A pplication of phosphorus (P) to agricultural lands can lead to increased offsite transport of P via surface runoff, erosion, and/or subsurface leaching to groundwater. Delivery of this P to P-sensitive water bodies can lead to water quality deterioration, primarily by accelerating the natural eutrophication process. Notable examples where excess P loading is contributing to water quality degradation include the Baltic Sea, Chesapeake Bay, the Florida Everglades, the Gulf of Mexico, and Lake Erie (Richardson et al., 2007;Chesapeake Bay Program, 2009;Dale et al., 2010;Andersson et al., 2014;Schoumans et al., 2014). In response to concerns over P losses from agricultural fields, research has focused on improving our understanding of the processes controlling P movement through the landscape (Radcliffe and Cabrera, 2007). This in turn has led to the development, improvement, and testing of models for predicting P fate and transport in the environment. When properly developed and used, these models can be useful tools for evaluating different management strategies for reducing P loss from agricultural fields (Sharpley et al., 2003;Radcliffe et al., 2009).Models for describing P movement through the landscape range in complexity depending on the theoretical rigor of the governing equations, the number of processes included in the mo...

Due to a shortage of available phosphorus (P)-loss datasets, simulated data from an accurate quantitative P transport model could be used to evaluate a P Index. The objective of this study was to compare predictions from the Texas Best Management Practice Evaluation Tool (TBET) against measured P-loss data to determine whether the model could be used to improve P Indices in the southern region. Measured P-loss data from field-scale study sites in Arkansas, Georgia, and North Carolina were used to assess the accuracy of TBET for predicting fieldscale loss of P. We found that event-based predictions using an uncalibrated model were generally poor. Calibration improved runoff predictions and produced scatterplot regression lines that had slopes near one and intercepts near zero. However, TBET predictions of runoff met the performance criteria (Nash-Sutcliffe efficiency ³ 0.3, percent bias £ 35%, and mean absolute error £ 10 mm) in only one out of six comparisons: North Carolina during calibration. Sediment predictions were imprecise, and dissolved P predictions underestimated measured losses. In North Carolina, total P-loss predictions were reasonably accurate because TBET did a slightly better job of predicting sediment losses from cultivated land. In Arkansas and Georgia, where the experimental sites were in forage production, the underprediction of dissolved P led directly to the underpredictions of total P. We conclude that TBET cannot be used to improve southern P Indices, but a curve number approach could be incorporated into P Indices to improve runoff predictions.Evaluation of the TBET Model for Potential Improvement of Southern P Indices Adam Forsberg, David E. Radcliffe,* Carl H. Bolster, Aaron Mittelstet, Daniel E. Storm, and Deanna Osmond G iven the limited amount of data available for evaluating and updating Phosphorus (P) Indices, the Southern Extension-Research Activity Group 17 (SERA-17) recommended that simulated output from P transport models be used as a substitute, provided the models have been shown to provide accurate estimates of P loss at the field scale . This approach has been successfully applied in multiple studies (Schoumans et al., 2002;Veith et al., 2005;Leone et al., 2008;Bolster et al., 2012). Bolster (2011) compared simulated P-loss data from the Annual Phosphorus Loss Estimator (APLE) against output from the Kentucky P Index. This analysis resulted in significant changes in the Kentucky P Index. Similarly, Bolster et al. (2012) used APLE to evaluate the Pennsylvania P Index. They demonstrated that APLE simulations could be used to derive more accurate P Index weighting factors and noted that correlating P Index ratings with quantitative P-loss model output can provide valuable estimates of uncertainty in the P Index.The Texas Best Management Practice Evaluation Tool (TBET) was developed by White et al. (2010White et al. ( , 2012White et al. ( , 2014 for conservation planners that needed a simple yet accurate tool to predict sediment and nutrient losses from agricultural fi...

The Agricultural Policy Environmental eXtender (APEX) model has been widely applied to assess phosphorus (P) loss in runoff water and has been proposed as a model to support practical decisions regarding agricultural P management, as well as a model to evaluate tools such as the P Index. The aim of this study is to evaluate the performance of APEX to simulate P losses from agricultural systems to determine its potential use for refinement or replacement of the P Index in the southern region of the United States. Uncalibrated and calibrated APEX model predictions were compared against measured water quality data from row crop fields in North Carolina and Mississippi and pasture fields in Arkansas and Georgia. Calibrated models satisfactorily predicted event-based surface runoff volumes at all sites (Nash-Sutcliffe efficiency [NSE] > 0.47, |percent bias [PBIAS]| < 34) except Arkansas (NSE < 0.11, |PBIAS| < 50) but did not satisfactory simulate sediment, dissolved P, or total P losses in runoff water. The APEX model tended to underestimate dissolved and total P losses from fields where manure was surface applied. The model also overestimated sediments and total P loads during irrigation events. We conclude that the capability of APEX to predict sediment and P losses is limited, and consequently so is the potential for using APEX to make P management recommendations to improve P Indices in the southern United States.