Shiny App for the ProbApple model

Shiny App for the ProbApple model Background Models, such as the ProbApple model are often hard to use without a proper user interface. Therefore we developed a Shiny App to run model. A detailed description of the model is published in the research article ProbApple – Aprobabilistic model to forecast apple yield and quality (Schmitz et al., 2025)1 In short, ProbApple is a probabilistic model to forecast apple yield and quality at four key time points during the vegetation period: ‘at full bloom’, ‘before fruit thinning’, ‘after June drop’, ‘four weeks before harvest’. Technical requirements To run the app locally on your computer, you need R and the packages shiny2, shinyWidgets3, datamods4, bslib5, reactable6, decisionSupport7, tidyverse8 and patchwork9. Repository content wwwis folder where all pictures needed in the App are stored. Shiny_App_ProbApple.Rproj is the R project file for running the app app.R is the main file with the Shiny App and contains the UI and the server part. apple_estimation_manuscript.csv and apple_quality_input_manucript.csv are files with the initial input parameters functions_v2.R contains the yield and quality prediction functions for all forecasting time points management_values.R, management_values1.R, management_values2.R and management_values3.R help to read in the choosen management measures for the calculation. Acknowledgments Thanks to Lars Zimmermann, Katja Schiffers, Martin Balmer and Eike Luedeling for their contribution in the development of ProbApple. Furthermore, i thank all experts who contributed their knowledge to the workshop and model building Funding This work was part of the Experimentierfeld Südwest funded by the German Federal Ministry of Food and Agriculture [grant number: 28DE111B22]. References Schmitz, C., Zimmerman, L., Schiffers, K., Balmer, M., Luedeling, E., 2025. ProbApple – A probabilistic model to forecast apple yield and quality. Agricultural Systems 226, 104298. ↩ Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2025). shiny: Web Application Framework for R. R package version 1.11.1, https://CRAN.R-project.org/package=shiny. ↩ Perrier V, Meyer F, Granjon D (2025). shinyWidgets: Custom Inputs Widgets for Shiny. R package version 0.9.0, https://CRAN.R-project.org/package=shinyWidgets. ↩ Perrier V, Meyer F, Goumri S, Abeer Z (2024). datamods: Modules to Import and Manipulate Data in 'Shiny'. R package version 1.5.3, https://CRAN.R-project.org/package=datamods. ↩ Sievert C, Cheng J, Aden-Buie G (2025). bslib: Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'. R package version 0.9.0, https://CRAN.R-project.org/package=bslib. ↩ Lin G (2023). reactable: Interactive Data Tables for R. R package version 0.4.4, https://CRAN.R-project.org/package=reactable. ↩ Luedeling E, Goehring L, Schiffers K, Whitney C, Fernandez E (2024). decisionSupport: Quantitative Support of Decision Making under Uncertainty. R package version 1.114, https://CRAN.R-project.org/package=decisionSupport. ↩ Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686. ↩ Pedersen T (2025). patchwork: The Composer of Plots. R package version 1.3.1, https://CRAN.R-project.org/package=patchwork. ↩