Southern Phosphorus Indices, Water Quality Data, and Modeling (APEX, APLE, and TBET) Results: A Comparison

Abstract

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...

Comparing an Annual and a Daily Time‐Step Model for Predicting Field‐Scale Phosphorus Loss

“…Correlations between model and measured risk ratings of TP loss were also similar for the two models (τ b = 0.28 and 0.27 for APLE and TBET, respectively; p < 0.001). Somewhat surprisingly, Osmond et al (2017) found that P Indices from several southern states were as accurate as these two models in assigning risk ratings to the same fields used in our study.…”

A Discussion on Mehlich-3 Phosphorus Extraction from the Perspective of Governing Chemical Reactions and Phases: Impact of Soil pH

“…The intended purpose of this test with respect to P is to extract portions of several different P pools that are correlated with the amount of P that will be available to plants over the growing season [1]. While not the original intent, M3-P has also been widely used for predicting the potential for non-point loss of dissolved and total P in runoff, leaching, and tile drainage to surface waters [2], as well as identifying locations that may be vulnerable to P loss (e.g., P indices; [3]). Understanding the factors that may influence M3-P is therefore critical for both agronomic and environmental applications.…”

Evaluation of Phosphorus Site Assessment Tools: Lessons from the USA

“…Similarly, Bolster et al (2017) showed that relatively poor predictions of total P loss with APLE were due, in part, to poor predictions of runoff and erosion. When using measured runoff and erosion data, model efficiencies for APLE increased from 0.52 to 0.62 for dissolved P and from −0.13 to 0.43 for total P. However, compared with measured P loads from 31 sites across Arkansas, Georgia, Mississippi, North Carolina, Oklahoma, and Texas, loads estimated by APLE and TBET, which were similar, were almost always higher than APEX estimates (Osmond et al, 2017). …”

“…Fate and transport models remain research tools that are not yet capable of providing accurate estimates of P loss under the diverse set of management scenarios and locations necessary to test or replace the current state‐by‐state system of P Indices. For instance, results from this assessment suggest that southern P Indices are just as robust as the harder‐to‐use fate and transport models (Osmond et al, 2017). In addition, several states have implemented revised indices in the last 5 yr: for example, Maryland (Maryland Department of Agriculture, 2016), Arkansas (DeLaune et al, 2006; Sharpley et al, 2010), Kentucky (Bolster et al, 2014), Tennessee (Walker and Hawkins, 2016), and Texas (White et al, 2012).…”

“…The potential of fate and transport models to provide accurate estimates of P loss for multiple locations, climates, and scenarios has led to widespread interest in evaluating water quality policy and program outcomes at a watershed scale (USEPA, 2010; Whittaker et al, 2015). Indeed, experience exists with “quantitative” P Indices, which leverage fate and transport to predict edge‐of‐field runoff under different management and site conditions (White et al, 2010; Good et al, 2012; Osmond et al, 2017). In these situations, nutrient management planners who lack expertise in fate and transport modeling use stripped‐down versions of fate and transport models for site assessment.…”