Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Feb 13;55(1):e70154. doi: 10.1002/jeq2.70154

APLE2026: Development of a graphical user interface for the Annual Phosphorus Loss Estimator model

Carl H Bolster 1,✉, Peter A Vadas 2
PMCID: PMC12905509  PMID: 41689285

Abstract

The Annual Phosphorus Loss Estimator (APLE) model is a commonly used annual time‐step model for predicting annual field‐scale surface runoff and erosion losses of dissolved and particulate P as well as annual changes in total and Mehlich‐3 extractable soil P. APLE was developed and coded as an Microsoft Excel workbook to provide a modeling option for users with limited modeling experience and lack of access to expensive software packages. The advantage of using Excel is that most users have both access and familiarity with Excel. However, the calculations within Excel require numerous calls to multiple cells, making it a challenge to modify and update the model. Moreover, the graphics are limited, and the current version does not have the ability to compare model predictions with observations. This limits the model's use and functionality. To address this, we have developed a Graphical User Interface application of APLE (APLE2026) to provide a cleaner and more intuitive user interface, improved graphics, and enhanced data analysis. This novel software package of APLE provides a more seamless way to run the model and view model output enhancing the functionality of APLE.

Plain Language Summary

We have developed a graphical user interface (GUI) for the commonly used Annual Phosphorus Loss Estimator (APLE) model. This new software package provides users with a cleaner interface, improved graphics, and basic data analysis tools. This results in a more seamless way to run the model and view model output allowing greater use of APLE.

Abbreviation

APLE

Annual Phosphorus Loss Estimator

1. INTRODUCTION

Phosphorus (P) is an essential nutrient for crop growth and is often applied to agricultural fields to maintain or maximize yields. Oftentimes, P is applied at rates that greatly exceed crop uptake requirements as insurance against nutrient deficiency. Even in soils with adequate P concentrations, P is often applied in the form of animal manures as a means of disposal (Sharpley et al., 2015). The overapplication of P can lead to accumulation of P in the soil over time. Because freshwater bodies are also often P‐limited, if this soil P is remobilized and transported to P‐limited waters, it can accelerate eutrophication leading to fish kills and reduced recreational value of the receiving waterbodies (Sharpley et al., 2013).

Models are often used to evaluate and quantify the risk of P runoff from agricultural fields during rain events. Models can also be used to evaluate how different management practices will affect soil P concentrations and runoff risk in the future. These models vary in their complexity, input requirements, outputs, and spatial and temporal scales, reflecting the model developers’ perceptual, conceptual, and procedural frameworks (Radcliffe et al., 2009; Vadas et al., 2013).

The Annual Phosphorus Loss Estimator (APLE) model, originally developed by Vadas et al. (2009) and updated by Bolster and Vadas (2022), is an annual time‐step model for predicting annual field‐scale surface runoff and erosion losses of dissolved and particulate P as well as annual changes in total and Mehlich‐3 extractable soil P. This model has been used extensively and has produced reasonably good predictions of both annual P loads and changes in soil test P at a variety of sites (Bolster et al., 2017, 2022; Vadas et al., 2012). The model has also been used to evaluate the effect of different management practices on P loss and soil P levels (Mott et al., 2025; Withers et al., 2019). Moreover, this model has been used to evaluate and improve P site indices—a simple tool used by many states to evaluate risks of P loss and to guide in P management strategies to protect water quality from P runoff from agricultural fields (Bolster, 2011; Bolster et al., 2014; Fiorellino et al., 2017).

To provide a modeling option for users with limited modeling experience and lack of access to expensive software packages, APLE was developed and originally coded in a Microsoft Excel workbook (Microsoft Corporation, 2025) However, the graphics are limited, and the current version does not have the ability to compare model predictions with observations. This limits the model's use and functionality. To address this, we have developed a Graphical User Interface application of APLE (APLE2026) to provide a cleaner and more intuitive user interface, improved graphics, and greater output capabilities.

2. SOFTWARE DESCRIPTION

APLE2026 was coded and compiled in MATLAB (The Mathworks, Inc. [2025]. Matlab version: 25.2.0 [R2025b]. Natick, Massachusetts) as a stand‐alone application that can be downloaded from the APLE Home Page (www.ars.usda.gov/APLE). To run APLE2026 without MATLAB requires downloading and installing the free software MATLAB Runtime from the MathWorks website (https://www.mathworks.com/help/compiler/about‐the‐matlab‐runtime.html). Alternatively, a customized version designed specifically for use with APLE2026 is included with the APLE2026 download. After downloading, extract Runtime into folder that contains the APLE2026 application. (see APLE2026 User's Manual for detailed instructions on installation.)

APLE2026 includes a collection of functions for importing data, calculating amount of P added to the soil by inorganic fertilizer or animal manure, and calculating the amount of added and existing P that is lost in surface runoff, soil erosion, or soil leaching. Required data to run the model are either imported from an Excel workbook (provided with download) or manually entered in the user interface. The software provides results in both graphical and tabular formats and allows users to calculate goodness‐of‐fit statistics when measured data are available. Color Universal Design was used when designing the user interface and figures.

3. ILLUSTRATIVE EXAMPLE

To demonstrate the use and capability of APLE2026, we use data collected from a study site located in the Riesel watershed in McLennan County, TX, that was part of a larger dataset used to evaluate the accuracy of the APLE model and compare it against the Texas Best Management Practice Evaluation Tool, a more complex daily time‐step model (Bolster et al., 2017).

3.1. Loading data

Data needed to run the model can either be imported from .xlsx or .csv files. Included with the download is an Excel workbook (APLE_input_data) that is properly formatted for use with APLE2026. (This workbook is distinct from the Excel version of APLE). The name of this workbook can be modified, but names of the worksheets within the file must not be modified. The workbook includes the following worksheets for users to supply data to run the model: fieldData, rainData, fertilizerData, solidManureData, liquidManureData, grazingManureData, and observedData. Multiple years of data can be entered into each worksheet. Greater detail can be found in the user's manual available on the APLE home page.

Once APLE2026 is opened, the user must select the method for calculating runoff (manually entered or calculated using the SCS‐curve number method) and the number of soil layers (1 or 2) prior to loading the data. Once these are entered into the interface, the user can load the data. Helper functions create and format data tables that are returned to the main software program. During this step, there are internal checks to identify erroneous data inputs. If found, the user is alerted by one or more warning messages that the user must correct before running the model. If no warnings are given, the Run APLE2026 button is enabled. Prior to running the software, the user must enter values for initial Mehlich‐3 equivalent soil test P and soil clay content in the user interface; otherwise, an error will be displayed, and the model will not run (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

To run the Annual Phosphorus Loss Estimator (APLE2026), the user must first select the runoff method and number of soil layers, after which data can be loaded into the software from a .xlsx or .csv file. Prior to running the model, the user must enter values for initial Mehlich‐3 equivalent soil test P (STP) and soil clay content.

3.2. Software output

After selecting the Run APLE2026 option, the model results are provided under the Results Tab. The first three tabs display plots of predicted dissolved, particulate, and total P loss; soil Mehlich‐3 content; and total soil P. On each of these plots, there is a button to add observed data (if applicable) to the plots (Figure 2). Selecting the P Loss Sources tab produces a stacked bar graph showing the contributions of different sources of P loss to the total P loss predictions (Figure 3). This allows land managers to identify the sources contributing most to predicted P loss to help guide management decisions. Selecting the P Drainage tab shows bar graph of model‐predicted P loads draining from soil layer 1 and, if applicable, soil layer 2 (Figure 4). Each of these figures has a button that, when pressed, creates a popout plot, which allows the user to export figure in various formats, including .jpeg, .png, and .pdf for later use. The Predicted Data Tab provides model predictions in tabular form, which can be saved in Microsoft Excel for further use.

FIGURE 2.

FIGURE 2

Open in a new tab

After running the model, results are presented in both graphical and tabular form. When measured data are available, the user can select the Plot Obs Data button to add the data to the figures. Shown above is the P Loss plot tab. Selecting Popout Plot button produces a separate figure that can be exported.

FIGURE 3.

FIGURE 3

Open in a new tab

Selecting the P loss sources ab produces stacked bar graphs showing the contributions of different sources of P loss to the total P loss predictions.

FIGURE 4.

FIGURE 4

Open in a new tab

Selecting the P drainage tab shows bar graphs of model‐predicted P loads draining from soil layers 1 and 2 (if applicable).

3.3. Data analysis

On the data analysis tab, users can select different buttons to compare model predictions with observations (Figure 5). Buttons are enabled when measured data are available. For instance, in the data set used here, soil P concentrations were not available (except for data to initialize model), so these buttons are not enabled. When selecting a button, a popup plot shows predicted versus measured data with a 1:1 line. A table also provides values of the root mean square error, mean absolute error, Nash‐Sutcliffe error, and bias (Figure 6). With a minimum of three observations, a regression line is automatically fit to the data with the slope, intercept, standard errors, and r 2 of the regression added to the table. As with the other plots, selecting the Save Plot button allows users to save the plot in various formats for later use.

FIGURE 5.

FIGURE 5

Open in a new tab

Model predictions can be compared with measured data by selecting the data analysis tab under the results tab. Only buttons where measured data are available are enabled. For the data set used in this example, there are no soil P data available and thus these buttons are not enabled.

FIGURE 6.

FIGURE 6

Open in a new tab

Selecting one of these buttons produces a popup figure with predicted and measured data, goodness‐of‐fit statistics, and results of regression on predicted versus observed data.

4. CONCLUSIONS AND IMPLICATIONS

APLE was originally developed as a simple spreadsheet model for predicting annual field‐scale dissolved and particulate P loss in surface runoff as well as annual changes in soil test P (Vadas et al., 2018, 2012). The model was designed to provide a relatively easy‐to‐use tool for those without significant modeling experience. However, APLE is currently only available as an Excel workbook with limited graphics and no option to compare predicted and observed data. To address this, we have developed a windows‐based desktop application of APLE (APLE2026) to provide a cleaner interface, improved graphics and output capabilities. This novel software package of APLE provides a more seamless way to run the model and view model output, thereby increasing the functionality of APLE. Future updates to APLE will only be incorporated into APLE2026, so APLE users are encouraged to transition from the Excel version to APLE2026.

AUTHOR CONTRIBUTIONS

Carl H. Bolster: Conceptualization; formal analysis; methodology; resources; software; validation; visualization; writing—original draft. Peter A. Vadas: Conceptualization; funding acquisition; writing—review and editing.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

ACKNOWLEDGMENTS

This research was conducted as part of USDA‐ARS National Program 212: Soil and Air. The manuscript is a contribution from the US Department of Agriculture's Conservation Effects Assessment Project (CEAP) National Legacy Phosphorus Assessment, with funding provided by USDA Natural Resources Conservation Service (NRCS) through an Interagency Agreement (NRC21IRA0010879) with the USDA Agricultural Research Service. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.

Bolster, C. H. , & Vadas, P. A. (2026). APLE2026: Development of a graphical user interface for the Annual Phosphorus Loss Estimator model. Journal of Environmental Quality, 55, e70154. 10.1002/jeq2.70154

Assigned to Associate Editor Adam Schreiner‐McGraw

REFERENCES

  1. Bolster, C. H. (2011). A critical evaluation of the Kentucky phosphorus index. Journal of the Kentucky Academy of Science, 72, 46–58. 10.3101/1098-7096-72.1.46 [DOI] [Google Scholar]
  2. Bolster, C. H. , Forsberg, A. , Mittelstet, A. , Radcliffe, D. E. , Storm, D. , Ramirez‐Avila, J. , Sharpley, A. N. , & Osmond, D. (2017). Comparing an annual and a daily time‐step model for predicting field‐scale phosphorus loss. Journal of Environmental Quality, 46, 1314–1322. 10.2134/jeq2016.04.0159 [DOI] [PubMed] [Google Scholar]
  3. Bolster, C. H. , Horvath, T. , Lee, B. D. , Mehlhope, S. , Higgins, S. , & Delgado, J. A. (2014). Development and testing of a new phosphorus index for Kentucky. Journal of Soil and Water Conservation, 69(3), 183–196. 10.2489/jswc.69.3.183 [DOI] [Google Scholar]
  4. Bolster, C. H. , & Vadas, P. A. (2022). Updates to the annual p loss estimator (APLE) model. Journal of Environmental Quality, 51(5), 1096–1102. 10.1002/jeq2.20378 [DOI] [PubMed] [Google Scholar]
  5. Bolster, C. H. , Wessel, B. M. , Vadas, P. A. , & Fiorellino, N. M. (2022). Sensitivity and uncertainty analysis for predicted soil test phosphorus using the annual phosphorus loss estimator model. Journal of Environmental Quality, 51, 216–227. 10.1002/jeq2.20328 [DOI] [PubMed] [Google Scholar]
  6. Fiorellino, N. M. , McGrath, J. M. , Vadas, P. A. , Bolster, C. H. , & Coale, F. J. (2017). Use of annual phosphorus loss estimator (APLE) model to evaluate a phosphorus index. Journal of Environmental Quality, 46(6), 1380–1387. 10.2134/jeq2016.05.0203 [DOI] [PubMed] [Google Scholar]
  7. Mott, J. , Simpson, Z. P. , Bolster, C. H. , Faulkner, J. , King, K. , Osterholz, W. , Williams, M. , Dalzell, B. , Feyereisen, G. W. , Dolph, C. L. , Miner, G. L. , Duriancik, L. F. , & Kleinman, P. J. A. (2025). Combining soil conservation with phosphorus drawdown can confront legacy phosphorus accumulation and transfer. Journal of Soil and Water Conservation, 80, 301–311. 10.1080/00224561.2025.2492525 [DOI] [Google Scholar]
  8. Radcliffe, D. E. , Freer, J. , & Schoumans, O. (2009). Diffuse phosphorus models in the United States and Europe: Their usages, scales, and uncertainties. Journal of Environmental Quality, 38(5), 1956–1967. 10.2134/jeq2008.0060 [DOI] [PubMed] [Google Scholar]
  9. Sharpley, A. , Jarvie, H. P. , Buda, A. , May, L. , Spears, B. , & Kleinman, P. (2013). Phosphorus legacy: Overcoming the effects of past management practices to mitigate future water quality impairment. Journal of Environmental Quality, 42, 1308–1326. 10.2134/jeq2013.03.0098 [DOI] [PubMed] [Google Scholar]
  10. Sharpley, A. N. , Bergström, L. , Aronsson, H. , Bechmann, M. , Bolster, C. H. , Börling, K. , Djodjic, F. , Jarvie, H. P. , Schoumans, O. F. , Stamm, C. , Tonderski, K. S. , Ulén, B. , Uusitalo, R. , & Withers, P. J. A. (2015). Future agriculture with minimized phosphorus losses to waters: Research needs and direction. Ambio, 44, S163–S179. 10.1007/s13280-014-0612-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Vadas, P. A. , Bolster, C. H. , & Good, L. W. (2013). Critical evaluation of models used to study agricultural phosphorus and water quality. Soil Use and Management, 29, 36–44. 10.1111/j.1475-2743.2012.00431.x [DOI] [Google Scholar]
  12. Vadas, P. A. , Fiorellino, N. M. , Coale, F. J. , Kratochvil, R. , Mulkey, A. S. , & McGrath, J. M. (2018). Estimating legacy soil phosphorus impacts on phosphorus loss in the chesapeake bay watershed. Journal of Environmental Quality, 47(3), 480–486. 10.2134/jeq2017.12.0481 [DOI] [PubMed] [Google Scholar]
  13. Vadas, P. A. , Good, L. W. , Moore, P. A., Jr., & Widman, N. (2009). Estimating phosphorus loss in runoff from manure and fertilizer for a phosphorus loss quantification tool. Journal of Environmental Quality, 38(4), 1645–1653. 10.2134/jeq2008.0337 [DOI] [PubMed] [Google Scholar]
  14. Vadas, P. A. , Joern, B. C. , & Moore, P. A. (2012). Simulating soil phosphorus dynamics for a phosphorus loss quantification tool. Journal of Environmental Quality, 41(6), 1750–1757. 10.2134/jeq2012.0003 [DOI] [PubMed] [Google Scholar]
  15. Withers, P. J. A. , Vadas, P. A. , Uusitalo, R. , Forber, K. J. , Hart, M. , Foy, R. H. , Delgado, A. , Dougherty, W. , Lilja, H. , Burkitt, L. L. , Rubæk, G. H. , Pote, D. , Barlow, K. , Rothwell, S. , & Owens, P. R. (2019). A global perspective on integrated strategies to manage soil phosphorus status for eutrophication control without limiting land productivity. Journal of Environmental Quality, 48, 1234–1246. 10.2134/jeq2019.03.0131 [DOI] [PubMed] [Google Scholar]

Articles from Journal of Environmental Quality are provided here courtesy of Wiley

RESOURCES