AquaCropPlotter: A Shiny app for visualizing and analyzing AquaCrop simulation results

Journal article

AquaCrop, a crop growth model developed by the Food and Agriculture Organization of the United Nations (FAO), can be used as a planning tool to assist management decisions in both irrigated and rainfed agriculture. AquaCrop simulates the yield of crops in response to water, particularly in conditions where water is a key limiting factor. AquaCrop balances accuracy, simplicity and robustness. However, visualization and analysis of its outputs can be a bottleneck since data generated from simulation runs are stored in a number of text files that contain a large amount of information. Processing these data can be challenging, especially at the high throughput commonly needed when comparing multiple models or assessing combinations of different factors. To address this limitation, we developed AquaCropPlotter, an R Shiny application designed to streamline the processing, visualization and analysis of AquaCrop outputs. The workflow of AquaCropPlotter starts with uploading a batch of all the AquaCrop output files selected by the user, followed by automated processing of the data into clean structured tables, which can then be explored with flexible visualization functionalities and simple statistical analysis tools. The resulting data tables, plots and analysis outputs can be readily exported for use in reports or further analysis. AquaCropPlotter is an open-source software available as a web application, locally installed application and R package. Its intuitive interface enables users to easily gain insights from AquaCrop simulation results without requiring programming expertise. As a case study, we applied AquaCropPlotter to analyze AquaCrop simulations conducted in the Republic of Moldova. Overall, AquaCropPlotter aims to facilitate broader utilization of AquaCrop and support its application across agricultural research and practice.

Authors: Sanguankiattichai, N; Setti, A; Alvar-Beltrán, J; Salman, M; Raes, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 20
• Issue: 12
• Article number: e0337705
• Publication date: 2025-12-02
• Acceptance date: 2025-11-12
• DOI: 10.1371/journal.pone.0337705
• EISSN: 1932-6203
• ISSN: 1932-6203
• Language: English