A short history of the phosphorus index and Andrew Sharpley's contributions from inception through development and implementation.

In the 1980s, growing recognition of agricultural phosphorus (P) sources to surface water eutrophication led to scrutiny of animal feeding operations. In 1990, the USDA-Natural Resources Conservation Service (NRCS) invited prominent scientists to find a solution. It was at an initial meeting that Dr. Andrew Sharpley suggested that P assessment could be modeled after the Universal Soil Loss Equation, where a matrix of factors influencing P loss would be associated with farm nutrient management recommendations. After codifying the P assessment into the USDA-NRCS 590 Nutrient Management Standard some 10 years later, 48 states chose to develop their own P Index. Sharpley, working with many others, helped develop several state P Indices. In 2000, Sharpley secured funding from the USDA-Agricultural Research Service to support the National P Research Project, which conducted in-field P runoff assessments using standardized rainfall simulated studies across 20 states; this allowed individual trials to be aggregated for agroecological regions that were then incorporated into specific state P Indices. Eventually, comparison of P Indices across state boundaries led to a white paper at the behest of USDA-NRCS that resulted in three regional projects evaluating modeling approaches to support or replace P Indices. Sharpley's national umbrella project pointed to shortcomings in water quality models, such as APEX or TBET, as a replacement for state P Indices, which remain a key part of the USDA-590 standard. As a selfless leader, capable of attracting and assembling diverse, productive interdisciplinary teams, Sharpley was essential to the inception, development, and implementation of the P Index.

Kinetics of phosphorous sorption to biochar-amended soils.

Biochar has been investigated as a potential soil amendment for increasing P sorption to soils. Several studies of shown that coating biochar with Fe oxides can increase the amount of P sorbed to the biochar, yet little is known about the kinetics of P sorption to soils amended with Fe-coated biochar. In this study, the kinetics of P sorption are measured in four soils with contrasting surface properties and textures. In addition, a wood-based biochar, both unmodified (BC) and modified by chemical precipitation of Fe oxides (BCFe), was added to these four soils at a rate of 5% (w/w). P sorption to each soil with and without the unmodified or Fe-coated biochar was measured at incubation times ranging from 1 to 314 h. The data were fit using five different kinetic models to determine if the addition of the BC or BCFe significantly affected the amount of P sorption and the kinetic behavior of P sorption to the biochar-amended soils. Results showed that amending with BC had minimal impact on P sorption to the four soils, whereas the impact of the BCFe on P sorption varied depending on soil. In the low P sorbing soil, the BCFe nearly doubled the amount of P sorbed whereas in the high P sorbing soil, addition of the BCFe resulted in less-than-expected increases in P sorption. For each biochar and soil treatment, the same kinetic model provided the best fit to the observed sorption over time. In two soils, the kinetic model parameters were significantly different following the addition of the BC whereas the model parameters for all four soils were significantly different following addition of BCFe. This study provides new insights into P sorption kinetics to biochar-amended soils.

Updates to the Annual P Loss Estimator (APLE) model.

The Annual P Loss Estimator (APLE) is a spreadsheet-based model developed for predicting annual field-scale P loss in surface runoff and changes in soil test P. This empirically based model was designed for use by those without significant modeling experience. However, a significant limitation with the model is that it does not calculate runoff. Moreover, APLE is deterministic and thus predicts a single value for a given set of inputs, thereby ignoring any uncertainties associated with model inputs. Here, we describe modifications to APLE that allow users to estimate runoff using the Curve Number method. Using Monte Carlo simulations, the updated version of APLE also provides users the ability to account for model input uncertainties in estimating model prediction errors. We provide examples of using the revised version of APLE (ver. 3.0) for calculating P loss from two fields in Mississippi over a 4-yr period and calculating the change in Mehlich-3 P concentrations over a 9-yr period at three locations in Maryland following cessation of P application. Both examples demonstrate that incorporating estimates of uncertainties in both measured data and model predictions provides modelers with a more realistic understanding of the model's performance.

Sensitivity and uncertainty analysis for predicted soil test phosphorus using the Annual Phosphorus Loss Estimator model.

In this study we conducted a sensitivity and uncertainty analysis using the Annual P Loss Estimator (APLE) model focusing on model predictions of soil test phosphorus (STP). We calculated and evaluated the sensitivity coefficients of predicted STP and changes in STP using 1- and 10-yr simulations with and without P application. We also compared two methods for estimating prediction uncertainties: first-order variance approximation (FOVA) and Monte Carlo simulation (MCS). Finally, we compared uncertainties in APLE-predicted STP with uncertainties in measured STP collected from multiple sites in Maryland under different manuring and cropping treatments. Results from our sensitivity analysis showed that predicted STP and changes in STP for 1-yr simulations without P inputs were most sensitive to initial STP, whereas model STP predictions were most sensitive to manure and fertilizer application rates when sensitivity analyses included P inputs. For the 10-yr simulations without P application inputs, the range in sensitivity coefficients for crop uptake and precipitation were much greater than for the 1-yr simulations. Prediction uncertainties from FOVA were comparable to those from MCS for model input uncertainties up to 50%. Using FOVA to calculate APLE STP prediction uncertainties using the Maryland data set, the mean measured STP for nearly all site years fell within the 95% confidence intervals of the STP prediction uncertainties. Our results provide users of APLE insight into what model inputs require the most careful measurement when using the model to predict changes in STP under conditions of P drawdown (i.e., no P application) or P buildup.

Coupled influences of particle shape, surface property and flow hydrodynamics on rod-shaped colloid transport in porous media.

HYPOTHESIS: Natural or engineered colloidal particles are often non-spherical in shape. In contrast to the widely-used "homogeneous sphere" assumption, the non-spherical particle shape is expected to alter particle-fluid-surface interactions, which in turn affect particle transport and retention. EXPERIMENTS AND SIMULATIONS: Polystyrene microspheres were stretched to rod-shaped particles of two aspect ratios (2:1, 6:1). The transport and retention behaviors of rods versus spheres were investigated in packed quartz sand columns and impinging jet systems. In parallel, a 3D trajectory model was employed to simulate particle translation and rotation, and to elucidate the role and underlying mechanisms of particle shape impact on transport. FINDINGS: Rods were observed to undergo rotating and tumbling motions in response to fluid shear from experiments and simulations. However, no distinct retention trends between rods and spheres were observed from column studies, despite BSA-coating on particles, Fe-coating on sand or velocity change. This was primarily due to the super-hydrophobic nature of colloid surfaces acquired from stretching process, which in hydrophilic sand columns, dominated particle-surface charge interactions. Simulations using colloids with randomly distributed charge patches qualitatively produced the observed insensitivity in retention respecting aspect ratio under low charge coverage (<30%). Hence, particle shape influences were strongly coupled with colloid surface properties and flow hydrodynamics.

Development of PLEAD: A Database Containing Event-based Runoff Phosphorus Loadings from Agricultural Fields.

Computer models are commonly used for predicting risks of runoff P loss from agricultural fields by enabling simulation of various management practices and climatic scenarios. For P loss models to be useful tools, however, they must accurately predict P loss for a wide range of climatic, physiographic, and land management conditions. A complicating factor in developing and evaluating P loss models is the relative scarcity of available measured field data that adequately capture P losses before and after implementing management practices in a variety of physiographic settings. Here, we describe the development of the P Loss in runoff Events from Agricultural fields Database (PLEAD)-a compilation of event-based, field-scale dissolved and/or total P loss runoff loadings from agricultural fields collected at various research sites located in the US Heartland and southern United States. The database also includes runoff and erosion rates; soil-test P; tillage practices; planting and harvesting rates and practices; fertilizer application rate, method, and timing; manure application rate, method, and timing; and livestock grazing density and timing. In total, >1800 individual runoff events-ranging in duration from 0.4 to 97 h-have been included in the database. Event runoff P losses ranged from <0.05 to 1.3 and 3.0 kg P ha for dissolved and total P, respectively. The data contained in this database have been used in multiple research studies to address important modeling questions relevant to P management planning. We provide these data to encourage additional studies by other researchers. The PLEAD database is available at .

Evaluation of Phosphorus Site Assessment Tools: Lessons from the USA.

Critical source area identification through phosphorus (P) site assessment is a fundamental part of modern nutrient management planning in the United States, yet there has been only sparse testing of the many versions of the P Index that now exist. Each P site assessment tool was developed to be applicable across a range of field conditions found in a given geographic area, making evaluation extremely difficult. In general, evaluation with in-field monitoring data has been limited, focusing primarily on corroborating manure and fertilizer "source" factors. Thus, a multiregional effort (Chesapeake Bay, Heartland, and Southern States) was undertaken to evaluate P Indices using a combination of limited field data, as well as output from simulation models (i.e., Agricultural Policy Environmental eXtender, Annual P Loss Estimator, Soil and Water Assessment Tool [SWAT], and Texas Best Management Practice Evaluation Tool [TBET]) to compare against P Index ratings. These comparisons show promise for advancing the weighting and formulation of qualitative P Index components but require careful vetting of the simulation models. Differences among regional conclusions highlight model strengths and weaknesses. For example, the Southern States region found that, although models could simulate the effects of nutrient management on P runoff, they often more accurately predicted hydrology than total P loads. Furthermore, SWAT and TBET overpredicted particulate P and underpredicted dissolved P, resulting in correct total P predictions but for the wrong reasons. Experience in the United States supports expanded regional approaches to P site assessment, assuming closely coordinated efforts that engage science, policy, and implementation communities, but limited scientific validity exists for uniform national P site assessment tools at the present time.

Use of Annual Phosphorus Loss Estimator (APLE) Model to Evaluate a Phosphorus Index.

The Phosphorus (P) Index was developed to provide a relative ranking of agricultural fields according to their potential for P loss to surface water. Recent efforts have focused on updating and evaluating P Indices against measured or modeled P loss data to ensure agreement in magnitude and direction. Following a recently published method, we modified the Maryland P Site Index (MD-PSI) from a multiplicative to a component index structure and evaluated the MD-PSI outputs against P loss data estimated by the Annual P Loss Estimator (APLE) model, a validated, field-scale, annual P loss model. We created a theoretical dataset of fields to represent Maryland conditions and scenarios and created an empirical dataset of soil samples and management characteristics from across the state. Through the evaluation process, we modified a number of variables within the MD-PSI and calculated weighting coefficients for each P loss component. We have demonstrated that our methods can be used to modify a P Index and increase correlation between P Index output and modeled P loss data. The methods presented here can be easily applied in other states where there is motivation to update an existing P Index.

Evaluation of the TBET Model for Potential Improvement of Southern P Indices.

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 field-scale 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 ≥ 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.

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

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 field-scale 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 ( < 0.001) correlation in both dissolved (ρ = 0.92) and particulate (ρ = 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.

Evaluation of the APEX Model to Simulate Runoff Quality from Agricultural Fields in the Southern Region of the United States.

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.

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

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.

Phosphorus fate, management, and modeling in artificially drained systems.

Phosphorus (P) losses in agricultural drainage waters, both surface and subsurface, are among the most difficult form of nonpoint source pollution to mitigate. This special collection of papers on P in drainage waters documents the range of field conditions leading to P loss in drainage water, the potential for drainage and nutrient management practices to control drainage losses of P, and the ability of models to represent P loss to drainage systems. A review of P in tile drainage and case studies from North America, Europe, and New Zealand highlight the potential for artificial drainage to exacerbate watershed loads of dissolved and particulate P via rapid, bypass flow and shorter flow path distances. Trade-offs are identified in association with drainage intensification, tillage, cover crops, and manure management. While P in drainage waters tends to be tied to surface sources of P (soil, amendments or vegetation) that are in highest concentration, legacy sources of P may occur at deeper depths or other points along drainage flow paths. Most startling, none of the major fate-and-transport models used to predict management impacts on watershed P losses simulate the dominant processes of P loss to drainage waters. Because P losses to drainage waters can be so difficult to manage and to model, major investment are needed (i) in systems that can provide necessary drainage for agronomic production while detaining peak flows and promoting P retention and (ii) in models that can adequately describe P loss to drainage waters.

Applicability of models to predict phosphorus losses in drained fields: a review.

Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P losses can occur in drainage water from artificially drained fields. In this review, we assess the applicability of nine models to predict this type of P loss. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P into runoff, macropore flow, and matrix flow. Within the soil profile, sorption and desorption of dissolved P and filtering of particulate P will be important. Eight models are reviewed (ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAMDB, PLEASE, and SWAT) along with P Indexes. Few of the models are designed to address P loss in drainage waters. Although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. ADAPT, HSPF, and most P Indexes do not simulate flow to tiles or drains. DRAINMOD simulates drains but does not simulate P. The ICECREAMDB model from Sweden is an exception in that it is designed specifically for P losses in drainage water. This model seems to be a promising, parsimonious approach in simulating critical processes, but it needs to be tested. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.

Future agriculture with minimized phosphorus losses to waters: Research needs and direction.

The series of papers in this issue of AMBIO represent technical presentations made at the 7th International Phosphorus Workshop (IPW7), held in September, 2013 in Uppsala, Sweden. At that meeting, the 150 delegates were involved in round table discussions on major, predetermined themes facing the management of agricultural phosphorus (P) for optimum production goals with minimal water quality impairment. The six themes were (1) P management in a changing world; (2) transport pathways of P from soil to water; (3) monitoring, modeling, and communication; (4) importance of manure and agricultural production systems for P management; (5) identification of appropriate mitigation measures for reduction of P loss; and (6) implementation of mitigation strategies to reduce P loss. This paper details the major challenges and research needs that were identified for each theme and identifies a future roadmap for catchment management that cost-effectively minimizes P loss from agricultural activities.

Stability and Transport of Graphene Oxide Nanoparticles in Groundwater and Surface Water.

The effects of groundwater and surface water constituents (i.e., natural organic matter [NOM] and the presence of a complex assortment of ions) on graphene oxide nanoparticles (GONPs) were investigated to provide additional insight into the factors contributing to fate and the mechanisms involved in their transport in soil, groundwater, and surface water environments. The stability and transport of GONPs was investigated using dynamic light scattering, electrokinetic characterization, and packed bed column experiments. Stability results showed that the hydrodynamic diameter of the GONPs at a similar ionic strength (2.1±1.1 mM) was 10 times greater in groundwater environments compared with surface water and NaCl and MgCl2 suspensions. Transport results confirmed that in groundwater, GONPs are less stable and are more likely to be removed during transport in porous media. In surface water and MgCl2 and NaCl suspensions, the relative recovery was 94%±3% indicating that GONPs will be very mobile in surface waters. Additional experiments were carried out in monovalent (KCl) and divalent (CaCl2) salts across an environmentally relevant concentration range (0.1-10 mg/L) of NOM using Suwannee River humic acid. Overall, the transport and stability of GONPs was increased in the presence of NOM. This study confirms that planar "carbonaceous-oxide" materials follow traditional theory for stability and transport, both due to their response to ionic strength, valence, and NOM presence and is the first to look at GONP transport across a wide range of representative conditions found in surface and groundwater environments.

Transport of , , and microspheres in biochar-amended soils with different textures.

The incorporation of biochar into soils has been proposed as a means to sequester carbon from the atmosphere. An added environmental benefit is that biochar has been shown to increase soil retention of agrochemicals, and recent research has indicated that biochar may be effective in increasing soil retention of bacteria. In this study we investigate the transport behavior of O157:H7, serovar Typhimurium, and carboxylated polystyrene microspheres in water-saturated column experiments for two soils (fine sand and sandy loam) amended with 2% poultry litter or pine chip biochars pyrolyzed at 350 and 700°C. Adding poultry litter biochar pyrolyzed at 350°C did not improve soil retention of either bacteria in fine sand and even facilitated their transport in sandy loam. Addition of either biochar pyrolyzed at 700°C generally improved retention of bacteria in fine sand, with the pine chip biochars being more effective in limiting their transport. Results from the column studies and auxiliary batch studies suggest that changes in cell retention after biochar amendments were likely due to changes in bacterial attachment in the column and not to physical straining or changes in survivability. We also found that changes in bacterial hydrophobicity after biochar amendments were generally correlated with changes in bacterial retention. The influence of biochar amendment in increasing retention of both bacteria was generally more pronounced in fine sand and indicates that soil texture affects the transport behavior of bacteria through biochar-amended soils.

Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model.

Models are often used to predict phosphorus (P) loss from agricultural fields. Although it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predictions of annual P loss by the Annual P Loss Estimator (APLE) model. Our objectives were (i) to conduct a sensitivity analyses for all APLE input variables to determine which variables the model is most sensitive to, (ii) to determine whether the relatively easy-to-implement first-order approximation (FOA) method provides accurate estimates of model prediction uncertainties by comparing results with the more accurate Monte Carlo simulation (MCS) method, and (iii) to evaluate the performance of the APLE model against measured P loss data when uncertainties in model predictions and measured data are included. Our results showed that for low to moderate uncertainties in APLE input variables, the FOA method yields reasonable estimates of model prediction uncertainties, although for cases where manure solid content is between 14 and 17%, the FOA method may not be as accurate as the MCS method due to a discontinuity in the manure P loss component of APLE at a manure solid content of 15%. The estimated uncertainties in APLE predictions based on assumed errors in the input variables ranged from ±2 to 64% of the predicted value. Results from this study highlight the importance of including reasonable estimates of model uncertainty when using models to predict P loss.

Phosphorus indices: why we need to take stock of how we are doing.

Many states have invested significant resources to identify components of their Phosphorus (P) Index that reliably estimate the relative risk of P loss and incentivize conservation management. However, differences in management recommendations and manure application guidelines for similar field conditions among state P Indices, coupled with minimal reductions in the extent of P-impaired surface waters and soil test P (STP) levels, led the U.S. Natural Resources Conservation Service (NRCS) to revise the 590 Nutrient Management Standard. In preparation for this revision, NRCS requested that a review of the scientific underpinnings and accuracy of current P Indices be undertaken. They also sought to standardize the interpretation and management implications of P Indices, including establishment of ratings above which P applications should be curtailed. Although some states have initiated STP thresholds above which no application of P is allowed, STP alone cannot define a site's risk of P loss. Phosphorus Indices are intended to account for all of the major factors leading to P loss. A rigorous evaluation of P Indices is needed to determine if they are directionally and magnitudinally correct. Although use of observed P loss data under various management scenarios is ideal, such data are spatially and temporally limited. Alternatively, the use of a locally validated water quality model that has been shown to provide accurate estimates of P loss may be the most expedient option to conduct Index assessments in the short time required by the newly revised 590 Standard.

Using a phosphorus loss model to evaluate and improve phosphorus indices.

In most states, the phosphorus (P) index (PI) is the adopted strategy for assessing a field's vulnerability to P loss; however, many state PIs have not been rigorously evaluated against measured P loss data to determine how well the PI assigns P loss risk-a major reason being the lack of field data available for such an analysis. Given the lack of P loss data available for PI evaluation, our goal was to demonstrate how a P loss model can be used to evaluate and revise a PI using the Pennsylvania (PA) PI as an example. Our first objective was to compare two different formulations-multiplicative and component-for calculating a PI. Our second objective was to evaluate whether output from a P loss model can be used to improve PI weighting by calculating weights for modified versions of the PA PI from model-generated P loss data. Our results indicate that several potential limitations exist with the original multiplicative index formulation and that a component formulation is more consistent with how P loss is calculated with P loss models and generally provides more accurate estimates of P loss. Moreover, using the PI weights calculated from the model-generated data noticeably improved the correlation between PI values and a large and diverse measured P loss data set. The approach we use here can be used with any P loss model and PI and thus can serve as a guide to assist states in evaluating and modifying their PI.